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Self-supervised learning for breast cancer detection: A review.

Hugo Figueiras1, José Domingues1, Nuno Matela2

  • 1LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal; Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Portugal.

Computers in Biology and Medicine
|October 25, 2025
PubMed
Summary

Self-supervised learning (SSL) advances breast cancer detection by reducing the need for labeled data in medical imaging. This review highlights SSL

Keywords:
Breast cancerImaging modalitiesMedical imagingSelf-supervised learning

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Machine learning for diagnostics

Background:

  • Breast cancer remains a leading global health concern, necessitating advancements in early detection and diagnosis.
  • Deep learning (DL) shows potential in computer-aided detection (CAD) but requires extensive labeled datasets.
  • Self-supervised learning (SSL) offers a solution by utilizing unlabeled data for robust feature learning.

Purpose of the Study:

  • To review the application of SSL in breast cancer detection across screening, diagnosis, grading, and staging.
  • To analyze SSL's impact on various imaging modalities like mammography, ultrasound, MRI, and histopathology.
  • To identify gaps and future directions for SSL in breast cancer imaging.

Main Methods:

  • Comprehensive literature review of SSL applications in breast cancer imaging.
  • Analysis of SSL's role in reducing annotation burden and improving model generalization.
  • Focus on modalities including mammography, digital breast tomosynthesis, ultrasound, MRI, and histopathology.

Main Results:

  • SSL shows success in mammography and ultrasound for early detection and in MRI/histopathology for lesion characterization.
  • SSL effectively reduces annotation demands and enhances generalization across different data domains.
  • A significant gap exists in SSL research for PET imaging within breast cancer diagnostics.

Conclusions:

  • SSL presents a transformative approach to breast cancer imaging, offering scalable solutions with reduced reliance on expert annotations.
  • Future research should explore SSL in underexplored modalities like PET and focus on multi-modal fusion and explainability.
  • SSL holds promise for improving breast cancer detection, characterization, and prognostication.